Open Source Lambda Architecture for deep learning
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The document presents an open-source architecture tailored for deep learning, integrating various components to optimize the training and testing of models. It highlights the significance of data quality and processing efficiency while accommodating big data frameworks and real-time data handling. Key components discussed include Apache Spark, Kafka, and PyTorch, alongside a flexible design for data processing across batch and streaming layers.
Open Source Lambda Architecture for deep learning
- 1. 𝜆 Open Source -Architecture for Deep Learning Use case Patrick R Nicolas Oct. 2020 pnicolasai@yahoo.com
- 2. Overview 3 “… and the wise man said, thou shall embrace open source”. 21st century proverb Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning
- 3. Overview 4 Overview Layers Open-source components Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning
- 4. Overview 5 The world of data scientists accustomed to Python scientific libraries have been shaken up by the emergence of ’big data’ framework such as Apache Hadoop, Spark and Kafka. This presentation introduces a variant of the architecture and describes the seamless integration of various open source components to train, validate and test deep learning models. Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning 𝜆
- 5. Disclaimer 6 The concept and architecture are versatile enough to accommodate a variety of open source, commercial solutions and services beside the frameworks prescribed in this presentation. For instance, deep learning frameworks, such as Keras or tensor flow are excellent alternatives to PyTorch. Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning
- 6. Requirements 7 • Process batch and stream data, concurrently • Enforce data immutability • Recover gracefully from human errors • Handle hardware failures • Minimize latency for real-time requests • Scale for very large data set • Optimize full lifecycle of data set • Guarantee quality and integrity of data Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning A ‘big data’ framework should be able to ….
- 7. Optimizing data life cycle 8 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning The need for optimizing the data life cycle: 79% of data scientist time is spent collecting and organizing data. Source Quora
- 8. Data quality 9 Accuracy: Correct models and representative data. Completeness: No missing data Consistency: Applied to semantic and format Timeliness: Up-to-date data and notification Accessibility: Ease of use and high availability Validity: Comply to constraints, rules and regulations Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Guaranteeing data quality and integrity
- 9. Solution … 10 - architecture is a large scale data processing that balanced batch and real-time streamed data. It is a one-stop shopping for various data sources that balance latency, redundancy, easy of access and throughput. It breaks down into 3 layers • Speed (streaming, real-time, …) • Batch (training, analysis, …) • Serving (query, visualization, …) Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning 𝜆
- 10. … using open source 11 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning architecture using open source components? 𝜆 The task consists of reviewing and evaluating the trove of available of open source libraries to build a robust architecture that support the rigor of training and tuning deep learning models. The libraries are weaved through a set language- agnostic REST API to form a coherent pipeline.
- 11. … for deep learning 12 • Python scientific libraries have been the go-to tools for data scientists to analyze data and build models. • PyTorch framework builds up on these libraries to support the design and execution of deep learning models. • Apache Spark and Kafka complements these frameworks for very large data set and real-time processing. Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning architecture for deep learning? 𝜆
- 12. Bird-eye view 13 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Feel overwhelmed? ... Let’s break it down Example open source 𝜆 architecture
- 13. Layers 14 Overview Layers Open-source components Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning
- 14. Batch layer 15 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Batch layer objective: load batch of data to be distributed, preprocessed to train deep learning models.
- 15. Batch layer 16 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Typical use case: 1. Apache Spark loads training set from Amazon S3 2. Spark master partitions training data 3. Spark workers preprocessed data and notify completion through Kafka event queue 4. Pytorch updated model parameters from pre- processed training data 5. Pytorch broadcast model parameters and quality metrics through Kafka 6. Apache Hive powered by Spark stores models related data and metrics
- 16. Speed layer 17 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Speed layer objective: process queries to predictive models with very low latency.
- 17. Speed layer 18 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Use case: 1. Kafka routes data streams to Spark master 2. Spark pre-processes requests and forward them to deep model micro-service 3. Flask converts requests to prediction query to Pytorch model 4. Pytorch model generate a prediction 5. Run-time metrics are broadcast through Kafka
- 18. Serving layer 19 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Serving layer objective: process queries to analyze data, model performances and execute statistical inference
- 19. Serving layer 20 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Use case: 1. Analyst queries relational data base, MySQL for most recent data, statistics using Fine report UI (low latency) 2. Analyst queries asynchronously Hive data warehouse for archived data, statistics (high latency) 3. Hive processes queries through Spark datasets 4. Spark updates regularly MySQL short term data
- 20. Overview 21 Overview Layers Open-source components Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning
- 21. PyTorch 22 PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. It extends the functionality of Numpy and Scikit- learn to support the training, evaluation and commercialization of complex machine learning models. https://pytorch.org/tutorials/ Alternatives: Tensor flow: https://www.tensorflow.org/ Keras: https://keras.io MxNet: https://mxnet.apache.org Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning
- 22. Apache Spark 23 Apache Spark is an open source cluster computing framework for fast real-time processing. It supports Scala, Java, Python and R programming languages and includes streaming, graph and machine learning libraries. https://www.scala-lang.org https://spark.apache.org Alternative: PySpark: https://databricks.com/glossary/pyspark Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning
- 23. Streaming 24 Apache Kafka is an open-source distributed event streaming framework to large scale, real-time data processing and analytics. It captures data from various sources in real-time as a continuous flow and routes it to the appropriate processor. https://kafka.apache.org Alternatives: Amazon SQS: https://aws.amazon.com/sqs/ RabbitMQ: https://www.rabbitmq.com Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning
- 24. Model tuning 25 Ray-tune is a distributed hyper-parameters tuning framework particularly suitable to deep learning models. It reduces significantly the cost of optimizing the configuration of a model. It is a wrapper around other open source libraries https://docs.ray.io/en/master/tune/index.html Alternatives: Amazon SageMaker: https://aws.amazon.com/sagemaker/ HyperOpt: https://github.com/hyperopt/hyperopt Optuna: https://optuna.readthedocs.io Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning
- 25. Python REST service 26 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Flask is an easy to use implementation of the RESTful interface to Python applications. It supports most of web and deployment standards such Docker, React.js, Angular, HTML5 and WSGI containers. https://palletsprojects.com/p/flask/ Alternatives: Falcon: https://falcon.readthedocs.io Fast API: https://fastapi.tiangolo.com
- 26. RDBMS 27 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning MySQL is an open source relational database supporting partitioning, sharding, replication. It can be extended with real-time analytics (Heatwave) and enterprise clustering (CGE) https://www.mysql.com Alternatives: PosgresSQL: https://www.postgresql.org HyperSQL http://www.hsqldb.org Amazon RDS: http://aws.amazon.com/rds
- 27. Data warehouse 28 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Apache Hive is a data warehouse framework that leverages Spark to execute largely distributed SQL queries. It optimizes SQL queries through lazy evaluation of acyclic execution graph. It is integrated with Spark data set and HDFS. https://hive.apache.org Alternatives: Vertica http://www.vertica.com Amazon Redshift https://aws.amazon.com/redshift/
- 28. Dashboard 29 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Fine report is a business intelligence and dashboard tool that supports real time analytics, reporting and visualization. It accomodates needs of business managers and data scientists https://www.finereport.com Alternatives: Sisense: https://www.sisense.com Tableau: https://www.tableau.com
- 29. 30 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Final disclaimer This presentation is not an endorsement of the various tools, libraries or frameworks described or suggested in this presentation. Allthough the tools listed in the slides are known to work in the context of the architecture, there are excellent alternative libraries that may better meet your specific needs.
- 30. 31 Patrick R. Nicolas - Open Source 𝜆 -Architecture for Deep Learning Thank you! Q&A